The recognition of three dimensional (3D) objects in point clouds is a challenging problem, with issues arising from discrete sampling, occlusions, and oftentimes cluttered scenes. Many existing methods use prior segmentation of 3D images or 3D descriptor training and matching, which are both time consuming and complex processes, especially for large-scale industrial or urban street data. For example, many existing systems select the best description for a specific type of 3D object so the objects can be recognized in a busy scene, and typically use prior segmentation of input data.
Still further, relatively few methods for 3D object recognition may be applied to an industrial scene. For example, in an industrial scene, objects are oftentimes more densely arranged, making segmentation more difficult. Regardless of the domain, most methods perform the recognition process in 3D, either using 3D local descriptors or exhaustive 3D scanning-window search. Both of these approaches typically use 3D descriptor or detector training and are time-consuming due to the 3D search, especially considering the millions or more 3D data points contained in industrial scenes. Furthermore, certain objects in an industrial scene do not remain stagnant, and it is oftentimes difficult to account for the differences.
Thus, there is a need in the art for an improved manner of recognizing objects and detecting changes.